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Semantic Image Synthesis with Spatially-Adaptive Normalization

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哲东 郑

yutian lin

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Semantic Image Synthesis with Spatially- Adaptive Normalization Taesung Park1∗ Ming-Yu Liu2 Ting-Chun Wang2 Jun-Yan Zhu2,3 1UC Berkeley 2NVIDIA 3MIT CSAIL https://nvlabs.github.io/SPADE/
Motivation • The conventional network architecture, which is built by stacking convolutional, normalization, and nonlinearity layers, is at best sub- optimal, because their normalization layers tend to “wash away” information in input semantic masks.
1. Replace the segmentation mask m with the image class label and making the modulation parameters spatially-invariant—— Conditional Batch Normalization Layer 2. Replace the segmentation mask with another image, making the modulation parameters spatially invariant and setting N = 1 —— AdaIN
Why does SPADE work better? • It can better preserve semantic information against common normalization layers
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization

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Scaling API-first – The story of a global engineering organization
 

Semantic Image Synthesis with Spatially-Adaptive Normalization

  • 1. Semantic Image Synthesis with Spatially- Adaptive Normalization Taesung Park1∗ Ming-Yu Liu2 Ting-Chun Wang2 Jun-Yan Zhu2,3 1UC Berkeley 2NVIDIA 3MIT CSAIL https://nvlabs.github.io/SPADE/
  • 2. Motivation • The conventional network architecture, which is built by stacking convolutional, normalization, and nonlinearity layers, is at best sub- optimal, because their normalization layers tend to “wash away” information in input semantic masks.
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  • 4. 1. Replace the segmentation mask m with the image class label and making the modulation parameters spatially-invariant—— Conditional Batch Normalization Layer 2. Replace the segmentation mask with another image, making the modulation parameters spatially invariant and setting N = 1 —— AdaIN
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  • 6. Why does SPADE work better? • It can better preserve semantic information against common normalization layers